A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets

By Ying Liu, W. Liao, Alok N. Choudhary. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2005

715 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning (2014, methodology): “Algorithms such as Two-Phase [8], IHUP [2] and UPGrowth [10] utilizes the aforementionned properties to prune the search space.”
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases (2009, methodology): “We have adopted definitions similar to those presented in the previous works [5], [6], [ 7 ], [8].”
USpan: an efficient algorithm for mining high utility sequential patterns (2012, methodology): “This is based on the sequence-weighted downward closure property (SDCP), which is similar to the transaction-weighted downward closure property (TDCP) in [8].”
Isolated items discarding strategy for discovering high utility itemsets (2008, methodology): “The parameters of the generator are introduced in [3] and modiﬁed in [27].”
Mining high utility itemsets without candidate generation (2012, methodology): “The three state-of-the-art algorithms had been proven to be superior to other algorithms, such as Two-Phase [18], ShFSM [12], DCG [11], FUM [14], and DCG+ [14].”